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Composite likelihood estimation method for hierarchical Archimedean copulas defined with multivariate compound distributions

Hélène Cossette, Simon-Pierre Gadoury, Etienne Marceau and Christian Y. Robert

Journal of Multivariate Analysis, 2019, vol. 172, issue C, 59-83

Abstract: We consider the family of hierarchical Archimedean copulas obtained from multivariate exponential mixture distribution through compounding, as introduced by Cossette et al. (2017). We investigate ways of determining the structure of these copulas and estimating their parameters. An agglomerative clustering technique based on the matrix of Spearman’s rhos, combined with a bootstrap procedure, is used to identify the tree structure. Parameters are estimated through a top-down composite likelihood. The validity of the approach is illustrated through two simulation studies in which the procedure is explained step by step. The composite likelihood method is also compared to the full likelihood method in a simple case where the latter is computable.

Keywords: Composite likelihood estimation; Hierarchical Archimedean copulas; Statistical collapsing strategy; Tree structure determination (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.jmva.2019.03.008

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